Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses limitations in traditional RDBMS for big data by allowing scaling to large clusters of commodity servers, high fault tolerance, and distributed processing. The core components of Hadoop are HDFS for distributed storage and MapReduce for distributed processing. Hadoop has an ecosystem of additional tools like Pig, Hive, HBase and more. Major companies use Hadoop to process and gain insights from massive amounts of structured and unstructured data.
Big Data with Hadoop and HDInsight. This is an intro to the technology. If you are new to BigData or just heard of it. This presentation help you to know just little bit more about the technology.
Facing trouble in distinguishing Big Data, Hadoop & NoSQL as well as finding connection among them? This slide of Savvycom team can definitely help you.
Enjoy reading!
Big Data with Hadoop and HDInsight. This is an intro to the technology. If you are new to BigData or just heard of it. This presentation help you to know just little bit more about the technology.
Facing trouble in distinguishing Big Data, Hadoop & NoSQL as well as finding connection among them? This slide of Savvycom team can definitely help you.
Enjoy reading!
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
Introductory Big Data presentation given during one of our Sizing Servers Lab user group meetings. The presentation is targeted towards an audience of about 20 SME employees. It also contains a short description of the work packages for our BIg Data project proposal that was submitted in March.
This presentation Simplify the concepts of Big data and NoSQL databases & Hadoop components.
The Original Source:
http://zohararad.github.io/presentations/big-data-introduction/
Hadoop has showed itself as a great tool in resolving problems with different data aspects as Data Velocity, Variety and Volume, that are causing troubles to relational database storage. In this presentation you'll learn what problems with data are occurring nowdays and how Hadoop can solve them . You'll learn about Hadop basic components and principles that make Hadoop such great tool.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
Introductory Big Data presentation given during one of our Sizing Servers Lab user group meetings. The presentation is targeted towards an audience of about 20 SME employees. It also contains a short description of the work packages for our BIg Data project proposal that was submitted in March.
This presentation Simplify the concepts of Big data and NoSQL databases & Hadoop components.
The Original Source:
http://zohararad.github.io/presentations/big-data-introduction/
Hadoop has showed itself as a great tool in resolving problems with different data aspects as Data Velocity, Variety and Volume, that are causing troubles to relational database storage. In this presentation you'll learn what problems with data are occurring nowdays and how Hadoop can solve them . You'll learn about Hadop basic components and principles that make Hadoop such great tool.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
As users gain more experience with Hadoop, they are building on their early success and expanding the size and scope of Hadoop projects. Syncsort’s third annual Hadoop Market Adoption Survey reflects the fact that Hadoop is no longer considered a technology for the future as it was when we first started conducting this research.
Get an in-depth look at the survey results and five trends to watch for in 2017. You’ll also learn:
• The best uses for Hadoop in 2017 – real-word examples of how Enterprises are realizing the value of Big Data
• Solutions to help you address the challenges enterprises still face in employing Hadoop
• What the future of Hadoop means for your business
This presentation introduces concepts of Big Data in a layman's language. Author does not claim the originality of the content. The presentation is made by compiling from various sources. Author does not claim copyrights or privacy issues.
Big data is exponentially rising in today's age of information and digital shrinkage. This presentation potentially clears the concept and revolving hype around it.
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Hadoop is one of the booming and innovative data analytics technology which can effectively handle Big Data problems and achieve the data security. It is an open source and trending technology which involves in data collection, data processing and data analytics using HDFS (Hadoop Distributed File System) and MapReduce algorithms.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
2. OUTLINE
• Data Generation Sources
• Per minute data evaluation
• What is Big Data?
• Limitations of RDBMS
• What is Hadoop?
• History of Hadoop
• Hadoop Core Components
• Hadoop Architecture
• Hadoop Ecosystem
3. OUTLINE
• Hadoop V1 v/s Hadoop V2
• Hadoop Distributions
• Who uses Hadoop?
• Overview of Data Lake
6. ’32 BILLION DEVICES PLUGGED IN &
GENERATING DATA BY 2020′
❑The EMC Digital Universe Study launched its seventh edition.
According to the study, by 2020, the amount of data in our
digital universe is expected to grow from 4.4 trillion GB to 44
trillion GB
-11th APRIL 2014
❑According to computer giant IBM, "2.5 exabytes - that's 2.5
billion gigabytes (GB) - of data was generated every day in
2012. That's big by anyone's standards. "About 75% of data is
unstructured, coming from sources such as text, voice and
video."
-Mr. Miles
7.
8. WHAT IS BIG DATA?
Gartner : “Big Data as high volume, velocity and
variety information assets that demand cost-
effective, innovative forms of information
processing for enhanced insight and decision
making.”
9. BIG DATA Volume, Velocity and Variety
Volume
• It refers to the vast amounts of data generated every
second.
Velocity
• Refers to the speed at which new data is generated and
the speed at which data moves around.
Variety
• Refers to the different types of data generated from
different sources.
10. BIG DATA HAS ALSO BEEN DEFINED
BY THE FIVE V’s
1.Volume
2.Velocity
3.Variety
4.Veracity
5.Value
11. BIG DATA Veracity
Veracity
• Veracity refers to the biases, noise and abnormality in
data.
• Is the data that is being stored, and mined meaningful
to the problem being analyzed.
12. BIG DATA Value
Value
• Data has intrinsic value—but it must be discovered.
• There are a range of quantitative and investigative
techniques to derive value from Big Data.
• The technological breakthrough makes much more
accurate and precise decisions possible.
• exploring the value in big data requires experimentation
and exploration. Whether creating new products or
looking for ways to gain competitive advantage.
14. Analysing
Big Data:
● Predictive
analysis
● Text analytics
● Sentiment
analysis
● Image
Processing
● Voice
analytics
● Movement
Analytics
● Etc.
Data Sources
● ERP
● CRM
● Inventory
● Finance
● Conversations
● Voice
● Social Media
● Browser logs
● Photos
● Videos
● Log
● Sensors
● Etc.
Volume
Veracity
Variety
Velocity
Turning Big Data into Value
Value
16. LIMITATIONS OF RDBMS TO SUPPORT
“BIG DATA”
• Designed and structured to accommodate structured
data.
• Data size has increased tremendously, RDBMS finds
it challenging to handle such huge data volumes.
• lacks in high velocity because it’s designed for steady
data retention rather than rapid growth.
• Not designed for distributed computing.
• Many issues while scaling up for massive datasets.
• Expensive specialized hardware.
• Even if RDBMS is used to handle and store
“BigData,” it will turn out to be very expensive.
18. WHAT IS HADOOP?
• Hadoop is an open-source software framework
• Allows the distributed storage and processing of large
data sets across clusters of commodity hardware
• uses simple programming models for processing
• It is designed to scale up from single servers to
thousands of machines.
each offering local computation and storage.
• It provides massive storage for any kind of data.
• enormous processing power and the ability to handle
virtually limitless concurrent tasks or jobs.
• Stores files in the form of blocks.
19. WHAT IS HADOOP?(cont)
• Hadoop is an open-source implementation of Google
MapReduce, GFS(Google File System).
• Hadoop was created by Dough Cutting, the creator of
Apache Lucene, the widely used text search library.
20. HISTORY OF HADOOP
• 2003 - Google launches project Nutch to handle billions of searches
and indexing millions of web pages.
• Oct 2003 - Google releases papers with GFS (Google File System).
• Dec 2004 - Google releases papers with MapReduce.
• 2005 - Nutch used GFS and MapReduce to perform operations.
• 2006 - Yahoo! created Hadoop based on GFS and MapReduce (with
Doug Cutting and team)
• 2007 - Yahoo started using Hadoop on a 1000 node cluster
• Jan 2008 - Apache took over Hadoop
• Jul 2008 - Tested a 4000 node cluster with Hadoop successfully
• 2009 - Hadoop successfully sorted a petabyte of data in less than 17
hours to handle billions of searches and indexing millions of web
pages.
21. HADOOP CORE COMPONENTS
HDFS(storage) and MapReduce(processing) are the two core
components of Apache Hadoop.
HDFS
• HDFS is a distributed file system that provides high-
throughput access to data.
• It provides a limited interface for managing the file system to
allow it to scale and provide high throughput.
• HDFS creates multiple replicas of each data block and
distributes them on computers throughout a cluster to enable
reliable and rapid access.
22. HADOOP CORE COMPONENTS
MapReduce
• MapReduce is a framework for performing distributed data
processing using the MapReduce programming paradigm.
• Each job has a user-defined map phase and user-defined
reduce phase where the output of the map phase is aggregated.
• HDFS is the storage system for both input and output of the
MapReduce jobs.
23. HDFS OVERVIEW
• Based on Google’s GFS (Google File System)
• Provides redundant storage of massive amounts of
data
– Using commodity hardware
• Data is distributed across all nodes at load time.
• Provides for efficient Map Reduce processing.
– Operates on top of an existing filesystem.
• Files are stored as ‘Blocks’
– Each Block is replicated across several Data Nodes
• NameNode stores metadata and manages access.
• No data caching due to large datasets
25. HADOOP ARCHITECTURE
NameNode
• Stores all metadata: filenames, locations of each block
on DataNodes, file attributes, etc…
• Keeps metadata in RAM for fast lookup.
• Filesystem metadata size is limited to the amount of
available RAM on NameNode
DataNode
• Stores file contents as blocks.
• Different blocks of the same file are stored on different
DataNodes.
• Periodically sends a report of all existing blocks to the
NameNode.
29. (CONT.)
❑Pig
❑A high-level data-flow language and execution
framework for parallel computation.
❑Hive
❑A data warehouse infrastructure that provides data
summarization and ad hoc querying.
❑Sqoop
❑A tool designed for efficiently transferring bulk data
between Hadoop and structured datastores such as
relational databases.
❑HBase
❑A scalable, distributed database that supports structured
data storage for large tables.
32. HADOOP DISTRIBUTIONS
➢Let's say we go download Apache Hadoop and
MapReduce from http://hadoop.apache.org/
➢At first it works great but then we decide to start using
HBase
➢No problem, just download HBase from
http://hadoop.apache.org/ and point to your existing
HDFS installation
➢But we find that HBase can only work with a previous
version of HDFS, so we go downgrade HDFS and
everything still works great.
➢Later on we decide to add Pig
➢Unfortunately the version of Pig doesn't work with the
version of HDFS, it wants us to upgrade
➢But if we upgrade we will break HBase.
33. HADOOP DISTRIBUTIONS
Hadoop Distributions aim to resolve version
incompatibilities
Distribution vendors will
Integration test a set of Hadoop product
Package Hadoop product in various installation
formats
linux packages, tarballs, etc
Distribution may provide additional scripts to execute
Hadoop
Some vendors may choose to backport features and
bug fixes made by Apache
Typically vendors will employ Hadoop committers so
the bugs find will make it into Apache repository.
34. DISTRIBUTION VENDORS
• Cloudera Distribution for Hadoop(CDH)
• MapR Distribution
• Hortonworks Data Platform (HDP)
• Greenplum
• IBM BigInsights
35. CLOUDERA DISTRIBUTION FOR
HADOOP(CDH)
• Cloudera has taken the lead on providing Hadoop
Distribution
• Cloudera is affecting the Hadoop ecosystem in the same
way RedHat popularized Linux in the enterprise circle
Most Popular Distribution
http://cloudera.com/hadoop
100% open-source
• Cloudera employs a large percentage of core Hadoop
committers
• CDH is provided in various formats Linux package,
Virtual Machine Images and Tarballs
• Integrates majority of popular Hadoop product
HDFS, MapReduce, HBase, Hive, Oozie, Pig, Sqoop,
Zookeeper and Flume etc.
36. SUPPORTED OPERATING SYSTEM
• Each Distribution will support its own list of Operating
System
• Common OS supported
Red Hat Enterprise
CentOS
Oracle Linux
Ubuntu
SUSE Linux Enterprise Server
38. OVERVIEW OF DATA LAKE
“A Data Lake is a large storage repository and processing
engine. They provide "massive storage for any kind
of data, enormous processing power and the ability to
handle virtually limitless concurrent tasks or jobs."